Where the Green Grows: Inequity and Urban Change
What if planting trees and building parks ended up making cities less equitable? As cities worldwide pursue sustainability initiatives to combat climate change and improve urban livability, these efforts have raised concerns about potential green gentrification. Green gentrification describes the process by which environmentally friendly urban improvements (parks, greenways, afforestation programs, etc.) unintentionally drive up property values and living costs, often displacing low-income and marginalized residents (Anguelovski, 2018). These projects—designed to enhance environmental quality and public health—frequently reinforce socioeconomic inequalities, benefiting wealthier newcomers and disparaging long-term residents with rising rent and property taxes. The concept of green gentrification is rooted in broader discussions of environmental justice, which examine how environmental benefits and burdens are distributed across different social groups. Historically, low-income and minority communities have been disproportionately affected by environmental hazards due to a lack of green space, urban vegetation, and adequate natural resources like water, air, and shade, among others (Mars 2020; Schell et al 2020). Urban greening initiatives are often framed as efforts to mitigate these disparities, yet they can paradoxically contribute to new forms of exclusion and displacement. Scholars argue that this occurs due to the commodification of nature, which emphasises green spaces as economic assets rather than essential public goods (Gould & Lewis, 2016). Additionally, municipal policies—such as zoning changes, tax incentives, and sustainability branding—play a role in shaping the audience that ultimately benefits from urban greening efforts (Anguelovski, 2022). This research investigates whether greening initiatives in the Twin Cities correlate with gentrification indicators, aiming to answer two key questions. How do urban greening initiatives in the Twin Cities contribute to processes of green gentrification? In an age of emerging green deals and comprehensive sustainable plans to meet climate action goals, it is the hope that our research can serve as a baseline to better understand green gentrification and the full spectrum of its effects. Further, this research will point towards the significance of requiring policy makers and urban planners to be more mindful of their implementation of greening and sustainable initiatives. Through this, these political actors and people in power can understand how to better serve the intended communities that need green and sustainable infrastructure the most.
Our research specifically focuses on the Twin Cities (Minneapolis, MN and St. Paul, MN), because this is a region that has advocated for their commitment to address climate change and developed sustainability initiatives and comprehensive plans to meet their goals in this. Both Minneapolis and Saint Paul have published 2040 master plans (City of Minneapolis, 2019; City of Saint Paul, 2020) that emphasize their commitments to creating climate resilient communities, addressing disparities in communities, increasing green space, green infrastructure, and tree canopy to mitigate greenhouse gas emissions and potential climate disasters. Examples of initiatives that aim to meet these goals include the Green Cities Accord, Twin Cities Climate Resiliency Initiative, Reconnect Rondo Restorative Development Project, and the Great River Greening Project.
The Twin Cities provides a unique context for studying green gentrification due to their history of racial and spatial inequality, including legacies of settler colonialism and systemic housing discrimination. The region has been shaped by practices such as redlining, which denied communities of color access to financial resources, leading to higher poverty rates in previously redlined neighborhoods. This historical context contributes to a segregated urban landscape, where environmental and economic disparities persist along racial and class lines (Schell et al., 2020). Today, as the region undergoes rapid urban redevelopment, these historically marginalized communities are increasingly vulnerable to displacement. This intersection of past injustice and present green redevelopment makes the Twin Cities an ideal site for studying how green improvements may unintentionally contribute to the negative consequences observed within gentrification efforts.
Within the Twin Cities, we chose seven ZIP codes to study. These were selected based on neighborhoods identified as gentrified in a University of Minnesota study (Goetz et al., 2019).
These include:
- Logan Park Neighborhood (55418)
- Sheridan Neighborhood (55413)
- East Phillips Neighborhood (55404 and 55407)
- Hamline-Midway Neighborhood (55104)
- Frogtown/Thomas-Dale Neighborhood (55103)
- Willard-Hay Neighborhood (55411)
We analyzed the appropriate ZIP code estimates to track changes that contributed to their classification as gentrified and assessed whether greening was associated with this. The goal, to understand the changes within these areas leading to their classification as gentrified and how/if greening could be associated with this. By examining the correlation between green space investments in the Twin Cities and demographic shifts, our study seeks to provide a nuanced understanding of how urban greening efforts intersect with social and economic inequities in the twin cities and if these efforts can contribute to intensifying the negative consequences of gentrification.
Further, by situating this research within broader discourse on environmental justice, we aim to highlight the importance of inclusive and community-driven planning strategies that prevent displacement while advancing urban greening initiatives. Understanding these dynamics is crucial for ensuring that the benefits of green infrastructure are equitably distributed, allowing all residents to participate in and benefit from sustainable urban development.
Figure 1. Map of Study Area
To calculate and understand gentrification, we will use the EasyCensus R package which contains data from the U.S. Census Tables and American Community Survey data (ACS) collected by the United States’ Census Bureau. The Census Bureau serves as the leading source of quality data about the nation’s people and economy. The U.S. Census Bureau conducts Annual Surveys of State and Local Government Finances as authorized by Title 13, United States Code, Section 9. The American Community Survey (ACS) is an ongoing survey monthly sent to a sample of addresses (about 3.5 million) in the 50 states, District of Columbia, and Puerto Rico which asks about topics not on the yearly Census, such as education, employment, internet access, and transportation. This helps to provide detailed information on a yearly basis about our nation and its people. Information from the survey generates data that help inform how trillions of dollars in federal funds are distributed each year (U.S. Census Bureau, n.d.).
From this, there were a couple data cleaning and composition steps before moving on to data analysis. Using the EasyCensus package, we were able to access the appropriate columns during the appropriate years using the functions cens_get_dec() to acquire decennial census data for year 2000 and cens_get_acs() for years 2013 and 2023. From these we were able to pick a subset of columns which would be appropriate for our data needs, and combine these into one dataset.
Figure 2. Extrapolated dataset
This data allowed us to track property value change, racial demographic change, income change, and changes in home vacancy rates which helped us compose our “Gentrification Index.” Using the data provided within the composite table, we calculated the percent change throughout the years and rated each on a 0-4 scale, 0 for no change, 1 for a slight change, 2 for a noticeable difference and 3 for a dramatic shift. The values which fall under 0 and 4 are variable dependent on their relativity to the other gentrified neighborhoods.
For example, Figure 3 below depicts how the variable “income_change” was calculated. The “pct_change” column calculates the percent change from the 2000 - 2013 interval and below that the 2013 - 2023 interval in that zip code (ZCTA) and the “avg_pct_change” column calculates the average percent change between the two percent changes. These are then manually scaled in comparison to each other in the red text. In this, the max percent change is deemed the most “drastic change” becoming the indicator of a “4” and the minimum percent change is closest to “no change” comparatively to other gentrified neighborhoods and from this we can compose intervals for the 0, 1, 2, 3, and 4 scores. In the case of income change, the difference between the maximum and the minimum was ~35, creating intervals of 7 each. 0 contained percent changes between 65 - 72, 1 contained percent changes between 72 - 79, 2 contained percent changes between 79 - 86, 3 contained percent changes between 86 - 93 and 4 contained percent changes between 93 - (approx.) 100.
Figure 3. Understanding Gentrification Index Scoring
We continued this scoring process with property value change, racial demographic change, and changes in home vacancy rates to compose Figure 4 below.
Figure 4. Gentrification Index Calculations
In order to understand changes in greenness through the years, we utilized Landsat 7 imagery at a spatial resolution of 30 meters, to calculate the Normalized Difference Vegetation Index (NDVI) through the Google Earth Engine. The NDVI index measures the difference between near-infrared and red light to build a ratio that classifies land cover on scale from -1.0-1.0 (Moreno, 2020). Near-infrared light is reflected with higher chlorophyll content detailing the health and “greenness” of the vegetation. Imagery will be collected from 2001, 2012, and 2022 using median composites. From this we can calculate the area of difference by using the amount of pixels that changed and converting it to acres. This will allow us to understand the proportion of the neighborhood that has changed in greenness.
To standardize NDVI change, we compared each gentrified ZIP code to a set of nearby non-gentrified neighborhoods, producing a baseline of average NDVI change. We then rated each ZIP code’s greenness change relative to this standard using the same 0–4 scoring system.
With these scores we were able to compose a final composite data set where we were able to track gentrification index alongside greenness change (column 9, figure 5 below).
Figure 5. Composite Gentrification Index and Greenness Table
We analyzed this dataset using the Tidyverse package in RStudio, visualizing and statistically analyzing the relationship using Pearson’s correlation coefficient, R² value, and p-value.
Figure 6. Visualizing Gentrification Index vs. Greenness
Figure 7. Calculating Statistic Information
From these graphs and values we can better understand these relationships. In investigating the research question “How have urban greening initiatives in the Twin Cities contributed to processes of green gentrification?” Our findings suggest that ZIP codes with above-average greenness improvements were moderately correlated (cor = 0.493) with higher gentrification scores. However, this correlation was not statistically significant (p = 0.3985), suggesting the relationship could be there but is definitly not conclusive. Additionally, we had a multiple R² value of 0.2432 meaning that approximately 24.3% of the variation in gentrification scores across ZIP codes can be explained by greenness change. This suggests a decent relationship, it also indicates that a lot of the variation is due to other factors not accounted for in the model. Thus, greening may be associated with demographic and economic shifts in these neighborhoods, although more research would need to be done regarding the details about this relationship and potentially, more variables would have to be utilized to better understand the complexities of this.
Three ZIP codes (55407, 55411, 55418) reported below-average or average greenness change with high Gentrification Index scores. These outliers may indicate the influence of non-environmental drivers of gentrification like nearby rapid job growth, or real estate investment. For example, in the case of zip codes 55413 and 55418, the Shrederin Neighborhood and Logan Park Neighborhood (respectively) which are both in Northeast Minneapolis we found an interesting contrast. We found that ZIP code 55413 showed both high greenness improvements and scored high on gentrification indicators. Meanwhile, nearby ZIP code 55418 experienced high gentrification and maintained standard greenness change. This suggests that even within the same general region, differences in local investment and community initiatives within Northeast Minneapolis has influenced the observed relationship between greenness and gentrification. Initiatives like the implementation of Sheridan Neighborhood community gardens, Neighborhood Clean up and other activities orchestrated by the active community members create dramatic change in a neighborhood (Sheridan Neighborhood Organization, n.d.).
One of the primary limitations of this study is the scale of the data used. Due to limitations in data availability, we were only able to analyze changes at the zip code level, rather than census blocks or block groups. This broader geographic lens could mask specific changes over time within neighborhoods that are critical for understanding how greening initiatives influence specific communities based on their specific community context. For example, while a zip code may show overall increases in greenness or score higher on the gentrification index, it doesn’t necessarily mean that the entire area within the zip code experiences these changes in the same way. Urban greening projects and gentrification sometimes occurs in pockets within a neighborhood and this pocket could experience improvements while others remain unchanged or even deteriorate. Without examining these patterns at a smaller scale, we cannot be sure that the greenness score and gentrification index accurately reflect the realities of all neighborhoods within the studied zip codes. Additionally, analyzing data at the zip code level may introduce variability that affects the accuracy of our findings. For instance, zip codes encompass multiple neighborhoods with potentially different levels of investment, gentrification, and/or access to green spaces. Our study may not fully capture the complexity of how greening initiatives interact with socioeconomic shifts at the community level. In future research, analysis at the census block or block group level would allow for a better understanding of how urban greening and gentrification can intersect within specific communities, shedding light on the disparities that may be obscured when using zip codes.
The use of the Normalized Difference Vegetation Index (NDVI) to quantify greenness may not capture the full spectrum of “green” improvements that could influence gentrification. NDVI is helpful for measuring vegetation; it does not account for other aspects of urban green spaces, such as tree canopy quality, accessibility, or diversity of plant species present. Greening initiatives that offer more than just vegetation, like community gardens or parks, cannot be fully represented by NDVI data, which primarily captures the broader vegetation coverage rather than the specific advantages that having these ecosystem services available may provide. With this, the study does not take into account the full range of non-environmental factors that can contribute to gentrification within the gentrification index because of data availability, in this, for future research having more gentrification indicators might be helpful.
Finally, while the study focuses on the Twin Cities, the findings may not be easily generalizable to other regions with different demographic or geographic characteristics. Local contexts shaped how greening initiatives are going to interact with gentrification. Therefore, while this research provides valuable insights into the Twin Cities, further studies in other cities with different socio-political landscapes are needed to assess the broader applicability of these findings.
Following our results, we cannot reject the null hypothesis that improvements in greenness are unrelated to increases in gentrification indicators in the Twin Cities. Although we observed a moderate positive correlation (cor = 0.493) between greenness change and gentrification index scores, this relationship was not statistically significant (p = 0.3985), and our model only explained 24.3% (R² = 0.2432) of the variation in gentrification outcomes. These results suggest that while there may be some connection between urban greening and gentrification, the relationship is complex and influenced by additional social, economic, and spatial variables not captured in our current model.
Additionally, zip codes with high gentrification scores and below-average to average greenness improvements indicate that gentrification in these areas may be driven by other factors. These could include real estate investment, job growth, or proximity to transit and amenities, rather than greening initiatives alone. The contrast observed between neighboring zip codes, 55413 and 55418 in Northeast Minneapolis, further highlights how locals’ dynamics’ and community-led efforts like the Sheridan Neighborhood’s greening projects can influence neighborhood change.
Our findings point to a need for further research at a smaller geographic scale, such as the block group or census block level, to better capture variation within specific neighborhoods. Future studies should also consider expanding the range of variables used to measure both greenness and gentrification. These could include factors like tree canopy quality, green space accessibility, and more detailed socio-economic shifts, in order to better understand how environmental improvements interact with urban change.
The need for this further research is urgent, while our study is specific to the Twin Cities, the broader implications speak to ongoing tensions in cities across the United States between environmental sustainability and social equity. As green infrastructure continues to expand, cities must also develop strategies that ensure long-term residents, especially those in historically marginalized communities, can stay in these places and benefit from these improvements.